Inspection of reticles using machine learning

ABSTRACT

Disclosed are methods and apparatus for inspecting a photolithographic reticle. A near field reticle image is generated via a deep learning process based on a reticle database image produced from a design database, and a far field reticle image is simulated at an image plane of an inspection system via a physics-based process based on the near field reticle image. The deep learning process includes training a deep learning model based on minimizing differences between the far field reticle images and a plurality of corresponding training reticle images acquired by imaging a training reticle fabricated from the design database, and such training reticle images are selected for pattern variety and are defect-free. A test area of a test reticle, which is fabricated from the design database, is inspected for defects via a die-to-database process that includes comparing a plurality of references images from a reference far field reticle image to a plurality of test images acquired by the inspection system from the test reticle. The reference far field reticle image is simulated based on a reference near field reticle image that is generated by the trained deep learning model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 119 of prior U.S.Provisional Application No. 62/611,321, filed 28 Dec. 2017, titled“Inspection of Reticles Using Machine Learning” by Hawren Fang et al.which application is herein incorporated by reference in its entiretyfor all purposes.

TECHNICAL FIELD OF THE INVENTION

The invention generally relates to the field of semiconductorinspection, such as reticle inspection. More particularly the presentinvention relates to die-to-database inspections and the like.

BACKGROUND

Generally, the industry of semiconductor manufacturing involves highlycomplex techniques for fabricating integrated circuits usingsemiconductor materials which are layered and patterned onto asubstrate, such as silicon. An integrated circuit is typicallyfabricated from a plurality of reticles. Initially, circuit designersprovide circuit pattern data or a design database, which describes aparticular integrated circuit (IC) design, to a reticle productionsystem, or reticle writer. The circuit pattern data is typically in theform of a representational layout of the physical layers of thefabricated IC device. The representational layout includes arepresentational layer for each physical layer of the IC device (e.g.,gate oxide, polysilicon, metallization, etc.), wherein eachrepresentational layer is composed of a plurality of polygons thatdefine a layer's patterning of the particular IC device. The reticlewriter uses the circuit pattern data to write (e.g., typically, anelectron beam writer or laser scanner is used to expose a reticlepattern) a plurality of reticles that will later be used to fabricatethe particular IC design.

Each reticle or photomask is generally an optical element containing atleast transparent and opaque regions, and sometimes semi-transparent andphase shifting regions, which together define the pattern of coplanarfeatures in an electronic device such as an integrated circuit. Reticlesare used during photolithography to define specified regions of asemiconductor wafer for etching, ion implantation, or other fabricationprocesses.

A reticle inspection system may inspect the reticle for defects that mayhave occurred during the production of the reticles or after use of suchreticles in photolithography. Due to the large scale of circuitintegration and the decreasing size of semiconductor devices, thefabricated devices have become increasingly sensitive to defects. Thatis, defects which cause faults in the device are becoming smaller.Accordingly, there is a continuing need for improved inspectiontechniques for monitoring characteristics of the reticle.

SUMMARY

The following presents a simplified summary of the disclosure in orderto provide a basic understanding of certain embodiments of theinvention. This summary is not an extensive overview of the disclosureand it does not identify key/critical elements of the invention ordelineate the scope of the invention. Its sole purpose is to presentsome concepts disclosed herein in a simplified form as a prelude to themore detailed description that is presented later.

In one embodiment, methods and apparatus for inspecting aphotolithographic reticle are disclosed. A near field reticle image isgenerated via a deep learning process based on a reticle database imageproduced from a design database, and a far field reticle image issimulated at an image plane of an inspection system via a physics-basedprocess based on the near field reticle image. The deep learning processincludes training a deep learning model based on minimizing differencesbetween the far field reticle images and a plurality of correspondingtraining reticle images acquired by imaging a training reticlefabricated from the design database, and such training reticle imagesare selected for pattern variety and are defect-free. A test area of atest reticle, which is fabricated from the design database, is inspectedfor defects via a die-to-database process that includes comparing aplurality of references images from a reference far field reticle imageto a plurality of test images acquired by the inspection system from thetest reticle. The reference far field reticle image is simulated basedon a reference near field reticle image that is generated by the traineddeep learning model. In a specific implementation, the test reticle andthe training reticle are a same reticle and the test images are acquiredfrom different areas of such same reticle than areas from which thetraining images are acquired.

In another aspect, the physic-based process is based on the Hopkinsmethod for producing the far field reticle image on an image plane ofthe inspection tool based on the near field reticle image, and the deeplearning process includes mapping the reticle database image to a nearfield reticle image that would be generated by light interacting with areticle that was fabricated with the design database. In anotherembodiment, the deep learning model is a convolutional neural network(CNN) that does not incorporate reticle image formation onto the imageplane.

In a further aspect, the deep learning model excludes simulatingperturbations in the far field reticle image caused by field-dependentchanges in the inspection tool and is independent of the inspectiontool. In another aspect, the deep learning model is trained by adjustingcertain parameters, including weights and/or bias values, of a pluralityof layers of the deep learning model so as to minimize differencesbetween the far field reticle images and the corresponding trainingreticle images. In one embodiment, the layers, in which adjustingoccurs, comprise convolutional layers with nonlinear activations. In afurther aspect, the deep learning model is trained without adjustingparameters in one or more low pass filtering layers for down samplingoperations.

In an alternative embodiment, the CNN includes one or more convolutionallayers for counterbalancing deviations between the reticle databaseimage and a physical reticle produced by such reticle database image,one or more layers for producing a plurality of down sampled images, andone or more layers for implementing a sparse representation for nearfield resolution. In another implementation, the test images are alignedwith the reference images, and a dynamic compensation process is appliedto the reference images with respect to the test images tocounterbalance variations, including focus fluctuations and/or fielddependent variations, in the inspection tool.

In an alternative embodiment, the invention pertains to an inspectionsystem for inspecting a photolithographic reticle. The system comprisingat least one memory and at least one processor that are configured toperform one or more of the above-described operations. In anotheraspect, the invention pertains to a computer readable medium havinginstruction stored thereon for performing one or more of theabove-described operations.

These and other aspects of the invention are described further belowwith reference to the figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagrammatic representation of a process for obtainingreference reticle images from a design database in accordance with oneembodiment of the present invention.

FIG. 2 illustrates a diagrammatic representation of a neuron of aconvolutional layer for receiving a receptive field of the reticledatabase image in accordance with an example implementation of thepresent invention.

FIG. 3 illustrates a (convolutional neural network (CNN) process forgenerating a reticle near field image from a reticle database image inaccordance with one embodiment of the present invention.

FIG. 4 illustrates a detailed CNN process for generating reticle nearfield images from a reticle database image in accordance with a specificimplementation of the present invention.

FIG. 5 illustrates a defect detection process in accordance with oneembodiment of the present invention.

FIG. 6 is a diagrammatic representation of an example inspection systemin which techniques of the present invention may be implemented

FIG. 7 provides a schematic representation of a photomask inspectionapparatus in accordance with certain embodiments.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of the present invention. Thepresent invention may be practiced without some or all of these specificdetails. In other instances, well known process operations have not beendescribed in detail to not unnecessarily obscure the present invention.While the invention will be described in conjunction with the specificembodiments, it will be understood that it is not intended to limit theinvention to the embodiments.

Inspection techniques described herein may be applied with respect toany suitable type of reticle or photomask. In one example, an extremeultraviolet (EUV) lithography process uses an EUV type reticle that isdesigned to facilitate patterning on a wafer at EUV wavelengths, such as13.5 nm. An EUV reticle may generally include a substrate, such a lowthermal expansion (LTE) or ultra-low expansion (ULE) glass plate, suchas fused silica. The substrate is covered with multiple layers ofmaterials to provide moderate reflectance (e.g., 60-70% or more) at theEUV wavelength for performing lithographic exposure at EUV wavelengths.The multilayer (ML) stack serves as a Bragg reflector that maximizes thereflection of EUV radiation while being a poor absorber of the EUVradiation. Reflection generally occurs at interfaces between materialsof different indices of refraction with higher differences causing morereflectivity. Although indices of refraction for materials exposed towavelengths that are extremely low are about equal to 1, significantreflection can be achieved through use of multiple layers havingalternating layers of different refractive indices. The ML stackcomprises low absorption characteristics so that the impinging radiationis reflected with little loss. In certain embodiments, the multiplelayers include between about 30 to 40 (or 40 to 50) alternating pairs ofmolybdenum (Mo) and silicon (Si) layers arranged with about 7 nanometerpitch. Other suitable layers may include alternating layers of Mo₂C andSi, Mo and beryllium (Be), molybdenum ruthenium (MoRu) and Be.

The multiple layers may include a capping layer, such as Ru, to preventoxidation. In other embodiments, an EUV reticle may include a quartz,antireflective coating (ARC), and other features. A pattern is formed inan absorber layer that is disposed over the multiple layers. Forexample, a tantalum boron nitride (TaBN) film topped by a thinantireflective oxide, such as tantalum boron oxide (TaBO), acts as anEUV absorber. The material(s) used for the reticle pattern may beselected to have nearly zero etch bias so as to achieve ultra-fineresolution features.

Besides an EUV type reticle, the terms “reticle” and “photomask” mayalso include a transparent substrate, such as glass, borosilicate glass,quartz, or fused silica having a layer of opaque material formedthereon. The opaque (or substantially opaque) material may include anysuitable material that completely or partially blocks photolithographiclight (e.g., deep UV). Example materials include chrome, molybdenumsilicide (MoSi), tantalum silicide, tungsten silicide, opaque MoSi onglass (OMOG), etc. A polysilicon film may also be added between theopaque layer and transparent substrate to improve adhesion. A lowreflective film, such as molybdenum oxide (MoO₂), tungsten oxide (WO₂),titanium oxide (TiO₂), or chromium oxide (CrO₂) may be formed over theopaque material.

The term reticle may refer to different types of reticles including, butnot limited to, a clear-field reticle, a dark-field reticle, a binaryreticle, a phase-shift mask (PSM), an alternating PSM, an attenuated orhalftone PSM, a ternary attenuated PSM, and a chromeless phaselithography PSM. A clear-field reticle has field or background areasthat are transparent, and a dark-field reticle has field or backgroundareas that are opaque. A binary reticle is a reticle having patternedareas that are either transparent or opaque. For example, a photomaskmade from a transparent fused silica blank with a pattern defined by achrome metal adsorbing film can be used. Binary reticles are differentfrom phase-shift masks (PSM), one type of which may include films thatonly partially transmit light, and these reticles may be commonlyreferred to as halftone or embedded phase-shift masks (EPSMs). If aphase-shifting material is placed on alternating clear spaces of areticle, the reticle is referred to as an alternating PSM, an ALT PSM,or a Levenson PSM. One type of phase-shifting material that is appliedto arbitrary layout patterns is referred to as an attenuated or halftonePSM, which may be fabricated by replacing the opaque material with apartially transmissive or “halftone” film. A ternary attenuated PSM isan attenuated PSM that includes completely opaque features as well.

Referring back to the EUV photolithography process, the light source mayproduce any suitable radiation that is suitable for use with EUVreticles. For instance, EUV wavelengths between about 11 to 14 nm orlower soft x-ray wavelengths may be utilized. In a specificimplementation, a wavelength of about 13.5 nm is produced. Duringphotolithography, radiation that is reflected from the multiple layersof an EUV reticle is absorbed in a resist layer formed on a wafersubstrate. The absorbed radiation produces photoacids (H+) and amplifiedphotoacids that form an exposed pattern in the resist layer of the wafersubstrate that corresponds to the absorber pattern layer of the EUVreticle when the photo resist is developed.

The defectivity control of the EUV photomasks, which defines thepatterns printed on silicon wafers, plays a critical role from a processyield management perspective. However, defect detection has beenregarded as one of the high risk areas of EUV lithography developmentdue to the lack of an actinic EUV photomask inspector that opticallyinspects the photomask at the same wavelength as the EUV scanner uses(e.g., 13.5 nm). Electron-beam inspection tools, which potentially canoffer a good sensitivity, typically have an inspection throughput thatis orders of magnitude slower than what is desired and is, therefore,not a practical solution for full mask inspection. Currently, and forthe foreseeable future, the inspection of patterned EUV photomasks hasto rely on the more available, higher throughput inspection toolsoperating within the deep-UV (DUV) wavelength range (190-260 nm).

One type of inspection technique utilizes a die-to-database approach,which typically includes calculating a reference image based on thedatabase. The database contains a list of polygonal shapes to be writtenon the reticle. Calculating the reference image may typically include(i) modeling electron-beam lithography by which the polygons are writtenon the reticle, (ii) characterizing the pupil illumination pattern ofthe reticle inspection microscope, (iii) calculating how theillumination interacts with the patterned reticle, forming a diffractednear field, and (iv) modeling how the diffracted near field is imagedand recorded by an array sensor at the image plane.

Calculating how the illumination interacts with the patterned reticle,forming a diffracted near field, is computationally difficult when usinga physics-based approach such as the Hopkins method. Generally, theHopkins method is based on the exchange of the integration order overthe point source contributions and the diffraction amplitudes, whichallows a given optical system with fixed illumination, numericalaperture, defocus, and other aberrations to be described with thetransmission cross coefficients (TCCs). The TCCs may be calculated justonce and thereafter reused for repeated image simulations of differentmask patterns imaged by the same optical system. One issue is that usingthe Hopkins method for an extreme ultra-violet (EUV) reticle iscomputationally prohibitively expensive. For instance, there is no knownmethod to solve rigorously for the entire reticle within a practicalinspection time. Another difficulty in calculating how the illuminationinteracts with the patterned reticle is that there is some uncertaintyin the dimensions of the reticle pattern, the profile of the sidewallsof the etched pattern, and the optical properties of reticle materials.

Certain embodiments of the present invention use machine learning to mapthe pattern on the mask to the diffracted field. In a specificimplementation, a convolutional neural network (CNN) is used to learnthis mapping of the mask pattern to the diffracted field, referred to asthe “mask near field.” Although the following example embodiments aremainly described in the context of a CNN, other machine learning orneural network processes can be utilized to learn the mask near fieldimage.

Except for the interaction of illumination with the reticle, imageformation can be accurately characterized and rapidly simulated using aphysics-based model. Accordingly, the approach for generating areference image for the reticle retains the physics-based model process.That is, the learning process, e.g., via CNN, does not incorporatereticle image formation onto the image plane, e.g., the inspection tooldetector.

In one embodiment, a convolutional neural network is trained usingactual reticles and their actual images acquired by the inspection tool.In this mode of operation, the training is performed by minimizing thedifference between the simulated final image on the detector and theactual image acquired by the tool.

Properties of the optical system can vary temporally and with differentpositions in the imaging field. Advantageously, once the fielddiffracted by the reticle is known from the CNN process, perturbationsin the image with respect to changes in the optical system can becomputed using a physics-based model. Accordingly, the CNN is notburdened with this task so that the training process is significantlysimplified as compared to if the detector image would be learned by theCNN. A neural network process provides a significantly more accuratecalculation of the near field reticle image as compared with aphysics-based approach. Additionally, the neural network process isefficient since the input and output images for the neural network arenot significantly different from each other and the learning task issimplified. This efficiency is accomplished by not having the neuralnetwork process learn the far field image. In sum, combining an accurateneural network approach for calculating a near field reticle image witha physics-based approach for calculating the reticle image from the nearfield reticle image results in a more efficient and sensitive inspectionprocess.

FIG. 1 is a diagrammatic representation of a process 100 for obtainingreference reticle images from a design database in accordance with oneembodiment of the present invention. Initially, a database image 102 isprovided to a deep learning model 104. The database image may beprovided based on the design database that was used to fabricate thereticle that is to be inspected. For example, a binary reticle image isrendered from the polygon descriptions of the design database using anysuitable techniques. That is, dark and light intensity values may beassigned to the absorber and multilayer areas, respectively or viceversa.

The deep learning model 104 is configured to generate a near fieldreticle image via a learning model into which the database reticle image102 is input. During the training, a near field reticle image is outputby the deep learning model 104 and input to a physics-based simulationprocess 106 based on physics-based modeling parameters 108. Forinstance, the physics-based approach involves the Hopkins method forsimulating the light from the reticle near field image passing throughthe inspection tool's collection path to a far field image on thedetector of such tool. The physics-based modeling parameters can includepre-calculating transmission cross coefficients (TCC) for the lightbehavior from the near field through the inspection system to thedetector. The physics-based simulation process 106 outputs a simulatedoptical reticle image, which is input to a training optimizer 110 thatoptimizes the differences between the optical reticle image and adefect-free training optical image 112 acquired by imaging a reticlefabricated from the design database.

In one embodiment, a small area of a reticle (e.g., less than 0.01%) ischecked and verified as defect-free or assumed to be defect-free. Imagesfrom this defect-free portion of the reticle are acquired and used astraining images. After training of the model, the rest of the reticle(e.g., other 99.99%) can be inspected using output from the trainedmodel.

The deep learning model works in conjunction with the training optimizer110 so as to train a thick mask diffraction model to output the nearfield reticle image from the database reticle image based on thedefect-free training image 112. That is, the optimizer trains or adjuststhe deep learning model parameters, while the physics-based modelingparameters 108 are kept constant.

Any suitable deep learning model may be used for determining the reticlenear field. In a specific implementation, a convolutional neural network(CNN) may be used. CNN is a class of deep, feed-forward artificialneural networks, most commonly applied to analyzing visual imagery soit's well suited for analyzing reticle database images. In general, theCNN includes multiple interconnected layers of “neurons”, and eachneuron is designed to mimic the visual cortex in that each neuronreceives and transforms only a small input field, e.g., a small portionof the reticle database image. The input area of a neuron is called itsreceptive field. In a convolutional layer, the receptive area is smallerthan the entire previous layer. FIG. 2 illustrates a diagrammaticrepresentation of a convolutional layer 204 having neuron 204 a forreceiving a receptive field 202 a of the reticle database image 202 inaccordance with an example implementation of the present invention. Thelayer 204 will also include other neurons (not shown) for otherreceptive fields of the reticle database image 202.

Each neuron in a neural network applies some function to the inputvalues from its receptive field in the previous layer and computes a setof output values. The function that is applied to the input values maybe specified by a vector of weights and a bias (typically real numbers).Learning in a neural network progresses by making incrementaladjustments to the biases and weights. In a convolutional layer, thevector of weights and bias may be referred to as a filter, and onedistinguishing feature of convolutional layers is that many neurons mayshare the same filter. This sharing of filters reduces memory footprintbecause a single filter may be used across all receptive fields, ratherthan each receptive field having its own bias and vector of weights in afully connected layer.

In neural networks, each neuron receives input from some number oflocations in the previous layer. In a fully connected layer, each neuronreceives input from every element of the previous layer. In aconvolutional layer, neurons receive input from only a restrictedsubarea of the previous layer. A fully connected layer for even a smallimage, such as 100×100 pixels, will entail use of 10000 weights for eachneuron in the receiving layer. Instead, use of a convolutional layerreduces the number of free parameters, allowing the network to be deeperwith fewer parameters. For instance, tiling regions of size 5×5 can eachuse the same shared weights, e.g., only 25 learnable parameters.

The CNN will typically have distinct types of layers, both locally andcompletely connected, which are stacked to form the CNN. These stackedlayers can be used to form an accuate reticle near field image from areticle database image. Several techniques for using CNN to learn anaccurate image are described further in, for example, Chao Dong et al.,“Learning a Deep Convolutional Network for Image Super-Resolution,” inProc. of European Conference on Computer Vision (ECCV), 2014, whichpaper is incorporated herein by reference. In general, the reticledatabase image will be input to the CNN layers to generate a near fieldimage, which is then used as input to a physic-based model that outputsa simulated reticle far field image (e.g., at the inspection detector).

FIG. 3 illustrates a simplified CNN process 300 for generating a reticlenear field image 312 from a reticle database image 302 in accordancewith one embodiment of the present invention. In this example, the CNNwill typically include additional layers and additional filters andmappings at each layer, which are not shown in the illustration so as tosimplify the description.

Initially, the reticle database image 302 may be received by a patchextraction layer 314 a for extracting and representing patches from thereticle database image 302. The patches may be overlapping. For example,patch extraction layer 314 a may include filter 304, which is in theform of a kernel of weights that are applied to each pixel. Each filterhas a size f₁×f₁. In the illustrated example, filter 304 is a 9×9 kernelthat is convolved with respect to each pixel of the reticle databaseimage 302. That is, the 9×9 kernel is stepped across the reticledatabase image so as to place each pixel one at a time into the centerof the kernel. In the current example, filter 304 is shown as beingapplied to pixel 302 b and its neighbor pixels 302 a of reticle databaseimage 302. The output of filter 304 on this pixel 302 b and its neighborpixels will be a value 306 a (of array 306). There will be additionalfilters (not shown) that are applied to each pixel, resulting in anarray of n1 values (e.g., array 306) for each pixel. A dot productbetween each kernel filter's weights and the respective overlappingreticle database image may generally be performed to result in a valuefor the particular pixel. A resulting image having a same size as thereticle database image is produced from the kernel being stepped andapplied across the entire reticle database image. The output arrays ofall the filters of the patch extraction layer as applied to all thereticle database image pixels are not shown so as to simplify theinvention.

As shown, the output from the patch extraction layer can then be inputto the next layer. As shown, a non-linear mapping layer 314 b may beapplied to the output of the patch extraction layer 314 a. As shown, theconvolution results n₁ (306) for reticle pixel 302 b may then be inputto a nonlinear mapping layer that outputs mapped nonlinear results n₂(308). Any suitable number and type of nonlinear mapping layer(s) may beused by the CNN. By way of examples, a sigmoid(x), a tan h(x), ReLU(rectified linear units), leaky ReLU, etc. The ReLU function applies thenon-saturating activation function f(x)={max(0,x)}, which serves toincrease the nonlinear properties of the decision function and of theoverall network without affecting the receptive fields of theconvolution layer.

After all the pixels of the reticle database go through the patchextraction and nonlinear mapping layers, a reconstruction process 314 cmay reconstruct a reticle near field image result 312 by combining thefilter and nonlinear mapping results. More precisely, the reconstructionprocess may aggregate f₃×f₃ neighboring patches for each pixel. Theresulting near field image may represent ground truth near field image.However, as further described herein, additional processing is performedon the near field image using a physics-based approach to simulate thefinal reticle far field image, which is expected to be similar to theground truth far field reticle image, which is readily available.

As mentioned herein, the CNN will likely include any number and type ofconvolution layers and/or other layers. FIG. 4 illustrates a detailedCNN process 400 for generating reticle near field images from a reticledatabase image in accordance with a specific implementation of thepresent invention. Initially, the reticle database images may comprisemultiple images 402. For example, the reticle database images 402 mayinclude an image that is rasterized from the design database, anabsolute-valued X gradient image denoted by |gradx(DB)|, and anabsolute-valued Y gradient image, denoted by |grady(DB)|. In thisexample, “DB” is the reticle database image. The gradient image in the Xdirection, |gradx(DB)|, can be implemented by the finite differenceapproximation, which is performed by a convolution filter [−1 0 1]applied to the rows of the DB subject to scaling. The absolute valuefunction may then be applied element-wise to form |gradx(DB)|. Likewise,the absolute-valued gradient image in the Y direction, |grady(DB)|, canbe obtained.

The CNN may include one or more convolutional layers forcounterbalancing the deviation between the design database image and thephysical mask. For example, a 1×1 kernel may be convolved across thereticle database images 402 to result in feature images 404, which maythen be input to another convolutional layer to produce a set of featureimages 406 that increase the number of images by applying multiplefilters to each feature image 404. That is, more than one filter may beapplied to each pixel of the counterbalanced images 404 to produce morefeature images 406 than the input feature images 404.

In one general counterbalancing example, summing up the reticle databaseimage and scaled |gradx(DB)| image has the effect of changing CD(critical dimension) of vertical line-space patterns. The |gradx(DB)|and |grady(DB)| may also be used to relax isometry (e.g., CD changes candiffer in the x- and y-directions). This is done because the e-beam maskwriters are not perfect, and the resulting mask can be slightlydifferent from the design. For example, if the design has 100 nm CD, thephysical mask could actually have 95 nm CD due to imperfections of themask writer. This deviation is often called “mask bias.” Thus, the 1stconvolution layer may serve to counter-balance this mask bias. Inpractice, this layer is related to, but not equivalent, to learning themask bias. In this example, this layer is configured to provide featureimage(s) that assist the learning and, therefore, improve the resultingaccuracy of the predicted near field reticle image.

The feature images 406 may then be input to a low pass filtering processfollowed by down-sampling the feature images 406 to a set of smallersized feature images 408. For example the feature images 406 may have a4× size, and the resulting feature images 408 have a 2× size. Ingeneral, the input images lack gray-scale and are almost binary (mostpixels are either completely bright or completely dark), whereas theexpected near field and far field images will be in fine gray-scale (asthe expected ground truth images). Hence, higher resolution featureimages (4×) with pixel size 55/4 nm square as input may be down-sampledthroughout the CNN to achieve a final output is 1× with pixel size 55 nmsquare. This 1^(st) down sampling layer may be configured to change thepixel size 55/4 nm square to 55/2 nm square (4× to 2×) by applying alow-pass filter from signal processing and then down-sampling. This is,different from average pooling, which is popular in the computer visiondomain.

The next layers 422 of the CNN 400 may implement a sparse representationfor a near field resolution. This neural network technique has beenapplied to image super-resolution in the above-referenced Chao Dongpaper and FIG. 3. For instance, a set of 9×9 filters are each convolvedacross the feature images 408 to result in feature images 410. Since the9×9 filters at this stage have a higher number than the input featureimages 408, the output feature images 410 (or output channels) also havea higher number than the input feature images 408. The number ofchannels output by each layer may be selected to provide significantimprovements in accuracy (i.e., more channels tend to result in higheraccuracy) balanced with conserving processing resources andgeneralization. For example, a higher channel number may not be selectedif it results in minimal improvement and increased processing, but ahigher number of channels are selected if they result in significantaccuracy improvements (even if increasing processing overhead). Thefilter number selection may be based on empirical results. The outputfeature images 410 may be smaller from using only the “valid” area,excluding halo area. The down-sampling layers of the CNN may contributeto the pixel size reduction to achieve the final 1× size as compared tothe reticle database image size 4×.

The feature images 410 are then received by a nonlinear layer, whichimplements a dimensional reduction process, so as to output a set offeature images 412 having a smaller number (channels) than the inputfeature images 410. In this example, the filters of size 1×1 areconvolved across the feature images 410 to output a reduced number ofchannels/feature images 412 having the same size as the input images.

A reconstruction layer as described above may then be applied to thefeature images 412 to output a set of near field residual images 414.The output of the (nonlinear) dimensionality reduction layer may bereferred to as “base images.” For reconstruction of each of the realpart and imaginary part of near field residual images, a 5×5 trainablefilter may be applied to each base image; summing up all filtered baseimages; followed by the nonlinear activation function so as to outputthe near field residual (either real part or imaginary part). The filtersize 5×5 is selected based on empirical results so as to achieve apredefined accuracy level although other sizes may be selected for otherapplications.

In practice, one CNN network may be used to predict 2 near field imagesfor improved computational efficiency. In this example, the near fieldimages are complex images, which can be represented by two real-valuedfeature images. The number of images may correspond to any number andtype of images that are produced for different inspection tool settings.In the current example, both X and Y polarization simulated images maybe produced by one CNN for use with X and Y polarization optical imagesacquired by the Teron 640e tool available from KLA-Tencor, Inc. ofMilpitas, Calif. Hence, in total there are 4 final reticle real-valuedimages forming two near field images. As shown, the near field residualimages 414 may then be added to Kirchoff's field, a linear approximationof the near field, to produce a set of 4 predicted real-valued images416 forming 2 near field images for two polarizations (X and Ypolarizations).

Kirchhoff's field is generally a linear approximation to the near field,fast but inaccurate. To assist training, the CNN may initially predictnear field residual images (the difference between the wanted near fieldand Kirchhoff's field). By summing the residuals with the Kirchhoff'sfield, the predicted near field images may then be obtained. In theillustrated example, the near field residual images 414 are added toKirchhoff's field to form the 2× near field residual images 416, whichare processed by a low pass filter, with the result down-sampled togenerate predicted near field images 418 at 1×.

Regardless of the specific CNN configuration, the predicted near fieldimages may be used to generate far field reticle images that are thenused to train the CNN, as described with respect to FIG. 1. For example,the predicted near field image (NF) may then be input to physics-basedsimulation process 106 based on physics-based modeling parameters 108.In one implementation, a partially coherent model (PCM) is configured togenerate the far field image by:

I _(model)(x,y)=Σ_(n=1) ^(N) |NF⊗φ _(n)|²  Equation [1]

where x,y is the pixel index; NF is the complex near field of the mask;and φ₁, φ₂, . . . φ_(n) are physical parameters of the inspection tool.

The optimizer 110 receives the generated model image I_(model) and thecorresponding training image I_(framing) (112), which are screened inadvance as being defect-free (via independent measurements). Anysuitable optimization process, such stochastic gradient descent (SGD),RMSProp, momentum, Adam, K-FAC, etc., may be used. The optimizer 110 maygenerally be configured to minimize the difference between such trainingimages I_(framing) (112) and the simulated far field images I_(model) byadjusting certain trainable parameters with respect to the deep learningmodel 104. In a specific implementation, a loss function, such as thesum of squares of the difference image as defined in the belowEquation[2], may be minimized:

Σ_(t=1) ^(# of training images)Σ_(x,y) ∥I _(model) ^((t))(x,y)−I_(training) ^((t))(x,y)∥_(F) ²  Equation [2]

In the above Equation [2], “∥ ∥_(F)” is the Frobenius norm. That is, thetrainable parameters of the deep learning model are adjusted until theloss function, e.g., of Equation [2], is minimized, after which the deeplearning model is deemed trained for the particular reticle. In the deeplearning model, the parameters of the convolutional layers areadjustable trainable parameters, except for the low pass filter layerswhich have fixed parameters that are not trained. In specificembodiment, the deep learning model may be trained soon after thereticle is manufactured and qualified as defect-free prior to being usedin a photolithography process. The training data may be selected andscreened by an experienced engineer with the objective to have asufficient coverage of defect-free patterns.

Once the deep learning model is trained for a particular reticle, it canthen be used as part of the overall defect detection process, whichincludes image alignment against the translational offset, and a dynamiccompensation (DC) mechanism to counterbalance the optical fluctuationand field dependency. FIG. 5 illustrates a defect detection process 500in accordance with one embodiment of the present invention. Forcompleteness, design database polygons may be initially received by a DBraster image rendering process 502 that generates reticle 4X database(DB) images, which are then received by deep learning process includinga deep learning model 504, such as a trained CNN as described above,which outputs predicted near field images.

An optical model image rendering process 506 receives the near fieldreticle images and is configured to produce far field images. An imagealignment process 508 is configured to align the test images acquiredfrom the inspection tool to the rendered far field image. To bespecific, the test images are moved relative to the far field imagesuntil there are minimal differences between the two sets of images.

Test images are obtained from the inspection area of a reticle.Moreover, an inspection tool may be operable to detect and collectreflected light images of multiple polarizations as an incident opticalbeam scans across each patch of the reticle. In non-EUV reticleinspectors, transmitted light (and/or reflected light) may be utilizedand the techniques described herein may be configured for suchinspectors. An incident optical beam may scan across reticle swaths thateach comprises a plurality of patches. Light is collected in response tothis incident beam from a plurality of points or subareas of each patch.

The inspection tool may be generally operable to convert such detectedlight into detected signals corresponding to intensity values. Thedetected signals may take the form of an electromagnetic waveform havingamplitude values that correspond to different intensity values atdifferent locations of the reticle. The detected signals may also takethe form of a simple list of intensity values and associated reticlepoint coordinates. The detected signals may also take the form of animage having different intensity values corresponding to differentpositions or scan points on the reticle. A reticle image may begenerated after all the positions of the reticle are scanned and lightis detected, or portions of a reticle image may be generated as eachreticle portion is scanned. In general, the test images from particularpatches of a reticle may be generated by the inspection tool.

In certain embodiments, a dynamic compensation (DC) process 512 may alsobe implemented on the far field images produced by the optical modelimage rendering process 506 with the aligned test images. The dynamiccompensation process may be configured to counterbalance certain toolvariations, such as focus fluctuations and field dependent variation, soas to produce dynamically compensated (DC) reticle images. The far fieldimages before and after dynamic compensation (DC) are called pre-DC andpost-DC images, respectively.

In this example, the pre-DC image may be defined by:

I _(model)=Σ_(n=1) ^(N)λ_(n) |NF⊗φ _(n)|²  Equation [3]

where NF is the complex near field of the mask; the eigen-pairs (λ₂,φ₂),(Δ₂,φ₂), . . . (λ₂,φ₂) are physical parameters of the inspection tool.

The dynamic compensation may then comprise minimization of another lossfunction. For instance, the linear least squares is:

$\begin{matrix}\min \\{\lambda_{1},\lambda_{2},\; {.\;.\;.}\mspace{14mu},\lambda_{n}}\end{matrix}{{I_{test} - I_{model}}}_{F}^{2}$

where the image I_(model) formed with the minimizer λ₁, λ₂, . . . λ_(n),rather than the original eigenvalues, is called the post-DC image.

Defect detection (510) may then be performed by comparing the post-DCreticle images to the test images and then an inspection report isobtained. The differences can then be reviewed, e.g., by a defectclassification process or high-resolution tool. For instance, a SEM maybe used to review the defective area to determine whether criticaldimensions (CD's) are out of specification. The review may includeseparating the nuisance defects from the “real” defects that will likelyimpact the function of the devices on the final wafer produced with suchreticle. By way of example, certain reticle nuisance defects may bedetermined to not likely result in printed defects on the wafer, whileothers may result in printed defects that do impact the device function.

An out-of-specification CD (or other defect) may result in the reticlenot passing the inspection. If the reticle fails inspection, the reticlemay be discarded or repaired if possible. For instance, certain defectscan be cleaned from the reticle. After repair or cleaning, a newinspection may be performed on the repaired or cleaned reticle and theprocedure repeated. Alternatively, the reticle fabrication process orreticle design may be adjusted, and a new reticle is fabricated.

In general, the absorber and multilayer materials of an EUV reticle formpattern structures that are designed and formed with critical dimension(CD) widths. A particular CD value may generally affect how a particularreticle feature is transferred to the wafer in the photolithographyprocess, and such CD is chosen to optimize this transfer process. Saidin another way, if a certain reticle feature's CD value is within aspecified CD range, such CD value will result in fabrication of acorresponding wafer feature that allows proper operation of theresulting integrated circuit, as intended by the circuit designer.Features are typically formed with minimum dimensions that also resultin operational circuits so as to conserve integrated chip area.

A newly fabricated reticle may include CD (or other film or patterncharacteristic) defect issues. For example, the reticle may havedefective CD regions. A reticle may become damaged over time in a numberof different ways. Some types of CD degradation may be caused bychemical reactions between the reticle features (MoSi) and the exposurelight, cleaning processes, contamination, etc. These physical effectscan also adversely affect the critical dimensions (CD's) of the reticleover time.

As a result of this degradation, the feature CD values may havesignificantly changed so as to affect CD uniformity across the reticleand adversely affect wafer yield. For instance, mask feature widths inportions of the mask may be significantly larger than the original linewidth CD. For instance, there may be a radial pattern of CDnon-uniformity, with the center of the reticle having different CD thanthe edges of the reticle.

A Critical-Dimension-Uniformity (CDU) map of a reticle may be generatedin order to facilitate monitoring of CD in such reticle. These CDU mapsmay be important for a semiconductor chip maker to understand theprocess window that will result from the use of the reticle. A CDU mapmay allow a chip maker to determine whether to use the reticle, applycompensation for the errors in the lithography process, or improvefabrication of a reticle so as to form an improved next reticle.

A CDU map may be generated using various techniques. In adie-to-database inspection approach, the average intensity valuesbetween corresponding areas of the test and reference images may becompared to obtain a delta intensity value. The delta-intensity valuesacross the reticle can then effectively form a delta-intensity map,which can then be calibrated to a full CDU map. Although the inspectiontechniques are described as being based on intensity type signals, othertypes of signals may be used in alternative embodiments of the presentinvention.

Certain embodiments of the present invention provide apparatus andtechniques for significantly improving the EUV photomask defectsensitivity of the DUV inspection tools by providing an efficiently andaccurately rendered reference reticle image from the design database. Byway of comparison, the deep learning technique for generating anaccurate near field image combined with a physics-based modellingprocess for generating a far field reticle image can be accomplished inless than 90 minutes, while using a physic-based rigorous simulationapproach to generate a near field image would take years. Resultsobtained with the combination deep learning and physics-based modellinghave been found to have significantly improved accuracy in terms of thedifference between the final modelled reticle image and the defect-freeimage, as compared to other techniques. To reduce the effect of shotnoise, a 2×2 convolution filter of all ones may be applied and themaximum difference in magnitude may be measured for estimating the modelerror. In an example of a 2K by 1K patch image with 2D patterns, themaximum convoluted difference between the pre-DC image and test image is51 gray scales with a deep learning approach, whereas the maximumconvoluted difference is 109 gray scales with a conventional approach.For the same patch image, maximum 88 gray-scale convoluted differencebetween the post-DC image and test image was found with a deep learningapproach versus 40 convoluted difference with conventional approach.Ideally, a deep learning process for generating the near field reticleimage is independent of the inspection tool.

Techniques of the present invention may be implemented in any suitablecombination of hardware and/or software. FIG. 6 is a diagrammaticrepresentation of an example inspection system 600 in which techniquesof the present invention may be implemented. The inspection system 600may receive input 602 from an inspection tool or scanner (not shown).The inspection system may also include a data distribution system (e.g.,604 a and 604 b) for distributing the received input 602, an intensitysignal (or patch) processing system (e.g., patch processors and memory606 a and 606 b) for processing specific portions/patches of receivedinput 602, a deep learning system (e.g., Deep Learning GPU and Memory612 a) for learning near field reticle images, a far field modeling andlearning support system (e.g., Far Field Modelling and Learning SupportProcessor and Memory 612 b), a network (e.g., switched network 608) forallowing communication between the inspection system components, anoptional mass storage device 616, and one or more inspection controland/or review stations (e.g., 610) for reviewing the maps. Eachprocessor of the inspection system 600 typically may include one or moremicroprocessor integrated circuits and may also contain interface and/ormemory integrated circuits and may additionally be coupled to one ormore shared and/or global memory devices. In a specific implementation,the deep learning model (e.g., CNN) is implemented on a graphicsprocessing unit (GPU) for increased speed and processing power for thetraining process, while the other processes for generating a post-DC orpre-DC reticle images (physics-based modeling, training optimization,dynamic compensation, etc.) are implemented by one or more CPU's andmemory.

The scanner or data acquisition system (not shown) for generating inputdata 602 may take the form of any suitable instrument (e.g., asdescribed further herein) for obtaining intensity signals or images of areticle. For example, the scanner may construct an optical image orgenerate intensity values of a portion of the reticle based on a portionof detected light that is reflected, transmitted, or otherwise directedto one or more light sensors. The scanner may then output the intensityvalues or image may be output from the scanner.

The reticle is generally divided into a plurality of patch portions fromwhich multiple intensity values from multiple points are obtained. Thepatch portions of the reticle can be scanned to obtain this intensitydata. The patch portions may be any size and shape, depending on theparticular system and application requirements. In general, multipleintensity values for each patch portion may be obtained by scanning thereticle in any suitable manner. By way of example, multiple intensityvalues for each patch portion may be obtained by raster scanning thereticle. Alternatively, the images may be obtained by scanning thereticle with any suitable pattern, such as a circular or spiral pattern.Of course, the sensors may have to be arranged differently (e.g., in acircular pattern) and/or the reticle may be moved differently (e.g.,rotated) during scanning in order to scan a circular or spiral shapefrom the reticle.

In the example illustrated below, as the reticle moves past the sensors,light is detected from a rectangular region (herein referred to as a“swath”) of the reticle and such detected light is converted intomultiple intensity values at multiple points in each patch. In thisembodiment, the sensors of the scanner are arranged in a rectangularpattern to receive light that is reflected and/or transmitted from thereticle and generate therefrom a set of intensity data that correspondsto a swath of patches of the reticle. In a specific example, each swathcan be about 1 to 2 million pixels wide and about 1000 to 2000 pixelshigh, while each patch can be about 2000 pixels wide and 1000 pixelshigh.

Intensity values for each patch may be obtained using an opticalinspection tool that is set up in any suitable manner. The optical toolis generally set up with a set of operating parameters or a “recipe”that is substantially the same for the different inspection runs forobtaining intensity values. Recipe settings may include one or more ofthe following settings: a setting for scanning the reticle in aparticular pattern, pixel size, a setting for grouping adjacent signalsfrom single signals, a focus setting, polarization setting, anillumination or detection aperture setting, an incident beam angle andwavelength setting, a detector setting, a setting for the amount ofreflected or transmitted light, aerial modeling parameters, etc.

Intensity or image data 602 can be received by data distribution systemvia network 608. The data distribution system may be associated with oneor more memory devices, such as RAM buffers, for holding at least aportion of the received data 602. Preferably, the total memory is largeenough to hold an entire swatch of data. For example, one gigabyte ofmemory works well for a swatch that is 1 million by 1000 pixels orpoints per setting of polarization and focus.

The data distribution system (e.g., 604 a and 604 b) may also controldistribution of portions of the received input data 602 to the patchprocessors (e.g. 606 a and 606 b). For example, data distribution systemmay route data for a first patch to a first patch processor 606 a, andmay route data for a second patch to patch processor 606 b. Multiplesets of data for multiple patches may also be routed to each patchprocessor. The distribution system may also control distribution ofportion of the modeled pre-DC or post-DC reticle images (“referencereticle images”) to the patch processors (e.g., 606 and 606 b).

The patch processors may receive intensity values or an image thatcorresponds to at least a portion or patch of the reticle. The patchprocessors may each also be coupled to or integrated with one or morememory devices (not shown), such as DRAM devices that provide localmemory functions, such as holding the received data portion. Preferably,the memory is large enough to hold data that corresponds to a patch ofthe reticle. For example, eight megabytes of memory works well forintensity values or an image corresponding to a patch that is 512 by1024 pixels. The patch processors may also share memory.

Each set of input data 602 may correspond to a swath of the reticle. Oneor more sets of data may be stored in memory of the data distributionsystem. This memory may be controlled by one or more processors withinthe data distribution system, and the memory may be divided into aplurality of partitions. For example, the data distribution system mayreceive data corresponding to a portion of a swath into a first memorypartition (not shown), and the data distribution system may receiveanother data corresponding to another swath into a second memorypartition (not shown). Preferably, each of the memory partitions of thedata distribution system only holds the portions of the data that are tobe routed to a processor associated with such memory partition. Forexample, the first memory partition of the data distribution system mayhold and route first data to patch processor 606 a, and the secondmemory partition may hold and route second data to patch processor 606b.

The incident light or detected light may be passed through any suitablespatial aperture to produce any incident or detected light profile atany suitable incident angles. By way of examples, programmableillumination or detection apertures may be utilized to produce aparticular beam profile, such as dipole, quadrapole, quasar, annulus,etc. In a specific example, Source Mask Optimization (SMO) or anypixelated illumination technique may be implemented.

The data distribution system may define and distribute each set of dataof the data based on any suitable parameters of the data. For example,the data may be defined and distributed based on the correspondingposition of the patch on the reticle. In one embodiment, each swath isassociated with a range of column positions that correspond tohorizontal positions of pixels within the swath. For example, columns 0through 256 of the swath may correspond to a first patch, and the pixelswithin these columns will comprise the first image or set of intensityvalues, which is routed to one or more patch processors. Likewise,columns 257 through 512 of the swath may correspond to a second patch,and the pixels in these columns will comprise the second image or set ofintensity values, which is routed to different patch processor(s).

FIG. 7 provides a schematic representation of an example inspectionsystem 750 that has illumination optics 751 a includes an imaging lenswith a relative large numerical aperture 751 b at a reticle plane 752 inaccordance with certain embodiments. The depicted inspection system 750includes detection optics 753 a and 753 b, including microscopicmagnification optics designed to provide, for example, 60-200×magnification or more for enhanced inspection. For example, thenumerical aperture 751 b at the reticle plane 752 of the inspectionsystem may be considerable greater than the numerical aperture 701 atthe reticle plane of the lithography system, which would result indifferences between test inspection images and actual printed images.Although the illustrated inspector includes both reflected andtransmitted light components (for inspection of non-EUV reticles), anEUV reticle inspection would only utilize reflected light.

The inspection techniques described herein may be implemented on variousspecially configured inspection systems, such as the one schematicallyillustrated in FIG. 7. The illustrated system 750 includes anillumination source 760 producing a light beam that is directed throughillumination optics 751 a onto a photomask M in the reticle plane 752.Examples of light sources include lasers or filtered lamps. In oneexample, the source is a 193 nm laser. As explained above, theinspection system 750 may have a numerical aperture 751 b at the reticleplane 752 that may be greater than a reticle plane numerical aperture ofthe corresponding lithography system. The photomask M to be inspected isplaced on a mask stage at the reticle plane 752 and exposed to thesource.

The patterned image from the mask M is directed through a collection ofoptical elements 753 a, which project the patterned image onto a sensor754 a. In a reflecting system, optical elements (e.g., beam splitter 776and detection lens 778) direct and capture the reflected light ontosensor 754 b. Suitable sensors include charged coupled devices (CCD),CCD arrays, time delay integration (TDI) sensors, TDI sensor arrays,photomultiplier tubes (PMT), and other sensors.

The illumination optics column may be moved respect to the mask stageand/or the stage moved relative to a detector or camera by any suitablemechanism so as to scan patches of the reticle. For example, a motormechanism may be utilized to move the stage. The motor mechanism may beformed from a screw drive and stepper motor, linear drive with feedbackposition, or band actuator and stepper motor, by way of examples.

The signals captured by each sensor (e.g., 754 a and/or 754 b) can beprocessed by a computer system 773 or, more generally, by one or moresignal processing devices, which may each include an analog-to-digitalconverter configured to convert analog signals from each sensor intodigital signals for processing. The computer system 773 typically hasone or more processors coupled to input/output ports, and one or morememories via appropriate buses or other communication mechanisms.

The computer system 773 may also include one or more input devices(e.g., a keyboard, mouse, joystick) for providing user input, such aschanging focus and other inspection recipe parameters. The computersystem 773 may also be connected to the stage for controlling, forexample, a sample position (e.g., focusing and scanning) and connectedto other inspection system components for controlling other inspectionparameters and configurations of such inspection system components.

The computer system 773 may be configured (e.g., with programminginstructions) to provide a user interface (e.g., a computer screen) fordisplaying resultant intensity values, images, and other inspectionresults. The computer system 773 may be configured to analyze intensitychanges, phase, and/or other characteristics of reflected and/ortransmitted sensed light beam. The computer system 773 may be configured(e.g., with programming instructions) to provide a user interface (e.g.,on a computer screen) for displaying resultant intensity values, images,and other inspection characteristics. In certain embodiments, thecomputer system 773 is configured to carry out inspection techniquesdetailed above

Because such information and program instructions may be implemented ona specially configured computer system, such a system includes programinstructions/computer code for performing various operations describedherein that can be stored on a computer readable media. Examples ofmachine-readable media include, but are not limited to, magnetic mediasuch as hard disks, floppy disks, and magnetic tape; optical media suchas CD-ROM disks; magneto-optical media such as optical disks; andhardware devices that are specially configured to store and performprogram instructions, such as read-only memory devices (ROM) and randomaccess memory (RAM). Examples of program instructions include bothmachine code, such as produced by a compiler, and files containinghigher level code that may be executed by the computer using aninterpreter.

In certain embodiments, a system for inspecting a photomask includes atleast one memory and at least one processor that are configured toperform techniques described herein. One example of an inspection systemincludes a specially configured TeraScan™ DUV inspection systemavailable from KLA-Tencor of Milpitas, Calif.

Although the foregoing invention has been described in some detail forpurposes of clarity of understanding, it will be apparent that certainchanges and modifications may be practiced within the scope of theappended claims. It should be noted that there are many alternative waysof implementing the processes, systems, and apparatus of the presentinvention. Accordingly, the present embodiments are to be considered asillustrative and not restrictive, and the invention is not to be limitedto the details given herein.

What is claimed is:
 1. A method of inspecting a photolithographicreticle, the method comprising: generating a near field reticle imagevia a deep learning process based on a reticle database image producedfrom a design database; simulating a far field reticle image at an imageplane of an inspection system via a physics-based process based on thenear field image, wherein the deep learning process includes training adeep learning model based on minimizing differences between the farfield reticle images and a plurality of corresponding training reticleimages acquired by imaging a training reticle fabricated from the designdatabase and such training reticle images are selected for patternvariety and are defect-free; and inspecting a test area of a testreticle, which is fabricated from the design database, for defects via adie-to-database process that includes comparing a plurality ofreferences images from a reference far field reticle image to aplurality of test images acquired by the inspection system from the testreticle, wherein the reference far field reticle image is simulatedbased on a reference near field image that is generated by the traineddeep learning model.
 2. The method of claim 1, wherein the test reticleand the training reticle are a same reticle and the test images areacquired from different areas of such same reticle than areas from whichthe training images are acquired.
 3. The method of claim 1, wherein thephysic-based process is based on the Hopkins method for producing thefar field reticle image on an image plane of the inspection tool basedon the near field image and wherein the deep learning process includesmapping the reticle database image to a near field image that would begenerated by light interacting with a reticle that was fabricated withthe design database.
 4. The method of claim 1, wherein the deep learningmodel is a convolutional neural network (CNN) that does not incorporatereticle image formation onto the image plane.
 5. The method of claim 4,wherein the deep learning model excludes simulating perturbations in thefar field image caused by field-dependent changes in the inspection tooland is independent of the inspection tool.
 6. The method of claim 4,wherein the deep learning model is trained by adjusting certainparameters, including weights and/or bias values, of a plurality oflayers of the deep learning model so as to minimize differences betweenthe far field reticle images and the corresponding training reticleimages.
 7. The method of claim 6, wherein the layers, in which adjustingoccurs, comprise convolutional layers with nonlinear activations.
 8. Themethod of claim 7, wherein the deep learning model is trained withoutadjusting parameters in one or more low pass filtering layers for downsampling operations.
 9. The method of claim 4, wherein the CNN includesone or more convolutional layers for counterbalancing deviations betweenthe reticle database image and a physical reticle produced by suchreticle database image, one or more layers for producing a plurality ofdown sampled images, and one or more layers for implementing a sparserepresentation for near field resolution
 10. The method of claim 1,further comprising: aligning the test images with the reference images;and applying a dynamic compensation process to the reference images withrespect to the test images to counterbalance variations, including focusfluctuations and/or field dependent variations, in the inspection tool.11. An inspection system for inspecting a photolithographic reticle, thesystem comprising at least one memory and at least one processor thatare configured to perform the following operations: generating a nearfield reticle image via a deep learning process based on a reticledatabase image produced from a design database; simulating a far fieldreticle image at an image plane of the inspection system via aphysics-based process based on the near field image, wherein the deeplearning process includes training a deep learning model based onminimizing differences between the far field reticle images and aplurality of corresponding training reticle images acquired by imaging atraining reticle fabricated from the design database and such trainingreticle images are selected for pattern variety and are defect-free; andinspecting a test area of a test reticle, which is fabricated from thedesign database, for defects via a die-to-database process that includescomparing a plurality of references images from a reference far fieldreticle image to a plurality of test images acquired by the inspectionsystem from the test reticle, wherein the reference far field reticleimage is simulated based on a reference near field image that isgenerated by the trained deep learning model.
 12. The system of claim11, wherein the test reticle and the training reticle are a same reticleand the test images are acquired from different areas of such samereticle than areas from which the training images are acquired.
 13. Thesystem of claim 11, wherein the physic-based process is based on theHopkins method for producing the far field reticle image on an imageplane of the inspection tool based on the near field image and whereinthe deep learning process includes mapping the reticle database image toa near field image that would be generated by light interacting with areticle that was fabricated with the design database.
 14. The system ofclaim 11, wherein the deep learning model is a convolutional neuralnetwork (CNN) that does not incorporate reticle image formation onto theimage plane.
 15. The system of claim 14, wherein the deep learning modelexcludes simulating perturbations in the far field reticle image causedby field-dependent changes in the inspection tool and is independent ofthe inspection tool.
 16. The system of claim 14, wherein the deeplearning model is trained by adjusting certain parameters, includingweights and/or bias values, of a plurality of layers of the deeplearning model so as to minimize differences between the far fieldreticle images and the corresponding training reticle images.
 17. Thesystem of claim 16, wherein the layers, in which adjusting occurs,comprise convolutional layers with nonlinear activations.
 18. The systemof claim 7, wherein the deep learning model is trained without adjustingparameters in one or more low pass filtering layers for down samplingoperations.
 19. The system of claim 14, wherein the CNN includes one ormore convolutional layers for counterbalancing deviations between thereticle database image and a physical reticle produced by such reticledatabase image, one or more layers for producing a plurality of downsampled images, and one or more layers for implementing a sparserepresentation for near field resolution.
 20. The system of claim 11,wherein the at least one memory and at least one processor are furtherconfigured for: aligning the test images with the reference images; andapplying a dynamic compensation process to the reference images withrespect to the test images to counterbalance variations, including focusfluctuations and/or field dependent variations, in the inspection tool.21. A computer readable medium having instruction stored thereon forperforming the following operations: generating a near field reticleimage via a deep learning process based on a reticle database imageproduced from a design database; simulating a far field reticle image atan image plane of an inspection system via a physics-based process basedon the near field image, wherein the deep learning process includestraining a deep learning model based on minimizing differences betweenthe far field reticle images and a plurality of corresponding trainingreticle images acquired by imaging a training reticle fabricated fromthe design database and such training reticle images are selected forpattern variety and are defect-free; and inspecting a test area of atest reticle, which is fabricated from the design database, for defectsvia a die-to-database process that includes comparing a plurality ofreferences images from a reference far field reticle image to aplurality of test images acquired by the inspection system from the testreticle, wherein the reference far field reticle image is simulatedbased on a reference near field image that is generated by the traineddeep learning model.